config updates

Author: codegithubka

docs/kimon_updates.md

@@ -261,7 +261,6 @@ Run with:
     python test_numba_optimized.py  # without pytest
 ```
 
-
 We have 48 tests cases validating the folloiwing:
 
 - Imports
@@ -295,6 +294,22 @@ Options:
 
 NOTE: Without the optimization kernels for a 1000x1000 grid the simulation (using 50 reps for statistical power) would run for 548 hours (approximately 23 days)
 
+### Results Dict
+
+Add clustering size index.
+Pertrubtion on the prey count.
+Cluster size distribution mesauremnt.
+
+powerlaw package.
+convergence of the disribution plots (p-values).
+
+Is the system SOC?
+
+Petrbutaions from initial conditions or critical point
+Timing between critical events? Get the model to be SOC.
+
+Look at larger collapes and the average collapse?
+
 
 
 

hpc/run_analysis.sh

@@ -1,6 +1,6 @@
 #!/bin/bash
 #SBATCH --job-name=pp_evo
-#SBATCH --partition=rome
+#SBATCH --partition=genoa
 #SBATCH --nodes=1
 #SBATCH --ntasks=1
 #SBATCH --cpus-per-task=32

scripts/numba_optimized.py

@@ -551,7 +551,6 @@ def compute_all_pcfs_fast(
 ) -> Dict[str, Tuple[np.ndarray, np.ndarray, int]]:
     """
     Compute all three PCFs using cell-list acceleration.
-    
     Returns dict with keys: 'prey_prey', 'pred_pred', 'prey_pred'
     Each value is (distances, pcf, n_pairs).
     """

scripts/pp_analysis.py

@@ -136,7 +136,6 @@ def cluster_interval(self) -> int:
     # Min density required for PCF/Clsuter Analysis
     min_analysis_density: float = 0.002 # FIXME: Minimum prey density (fraction of grid) to analyze clusters/PCF
 
-    
     target_prey_birth: float = 0.22  # FIXME: Change after obtaining results
     target_prey_death: float = 0.04  # FIXME; Change after obtaining results
 
@@ -192,7 +191,6 @@ def estimate_runtime(self, n_cores: int = 32) -> str:
 # HELPER FUNCTIONS
 # =============================================================================
 
-
 def generate_unique_seed(pb: float, pd:float, rep:int) -> int:
     """Creates a unique, deterministic seed from parameters."""